scholarly journals Spatio-Temporal Missing Data Imputation for Smart Power Grids

2021 ◽  
Author(s):  
Sanmukh R. Kuppannagari ◽  
Yao Fu ◽  
Chung Ming Chueng ◽  
Viktor K. Prasanna
Keyword(s):  
Missing Data ◽  
Power Grids ◽  
Data Imputation ◽  
Smart Power ◽  
Missing Data Imputation ◽  
Smart Power Grids ◽  
Spatio Temporal

Missing Data Imputation and Meta-analysis on Correlation of Spatio-Temporal Weather Series Data

2021 ◽  
Author(s):  
Alkiviadis Kyrtsoglou ◽  
Dimara Asimina ◽  
Dimitrios Triantafyllidis ◽  
Stelios Krinidis ◽  
Konstantinos Kitsikoudis ◽  
...  
Keyword(s):  
Missing Data ◽  
Meta Analysis ◽  
Series Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Spatio Temporal

Missing data imputation for traffic flow speed using spatio-temporal cokriging

2018 ◽  
Vol 88 ◽  
pp. 124-139 ◽  
Author(s):  
Bumjoon Bae ◽  
Hyun Kim ◽  
Hyeonsup Lim ◽  
Yuandong Liu ◽  
Lee D. Han ◽  
...  
Keyword(s):  
Missing Data ◽  
Traffic Flow ◽  
Flow Speed ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Spatio Temporal

scholarly journals Spatio-temporal missing data reconstruction in satellite displacement measurement time series

2020 ◽  
Author(s):  
Alexandre Hippert-Ferrer ◽  
Yajing Yan ◽  
Philippe Bolon
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Missing Values ◽  
Satellite Image ◽  
Displacement Measurement ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Spatial Coverage ◽  
Spatio Temporal ◽  
Offset Tracking

<p>Time series analysis constitutes a thriving subject in satellite image derived displacement measurement, especially since the launching of Sentinel satellites which provide free and systematic satellite image acquisitions with extended spatial coverage and reduced revisiting time. Large volumes of satellite images are available for monitoring numerous targets at the Earth’s surface, which allows for significant improvements of the displacement measurement precision by means of advanced multi-temporal methods. However, satellite image derived displacement time series can suffer from missing data, which is mainly due to technical limitations of the ground displacement computation methods (e.g. offset tracking) and surface property changes from one acquisition to another. Missing data can hinder the full exploitation of the displacement time series, which can potentially weaken both knowledge and interpretation of the physical phenomenon under observation. Therefore, an efficient missing data imputation approach seems of particular importance for data completeness. In this work, an iterative method, namely extended Expectation Maximization - Empirical Orthogonal Functions (EM-EOF) is proposed to retrieve missing values in satellite image derived displacement time series. The method uses both spatial and temporal correlations in the displacement time series for reconstruction. For this purpose, the spatio-temporal covariance of the time series is iteratively estimated and decomposed into different EOF modes by solving the eigenvalue problem in an EM-like scheme. To determine the optimal number of EOFs modes, two robust metrics, the cross validation error and a confidence index obtained from eigenvalue uncertainty, are defined. The former metric is also used as a convergence criterion of the iterative update of the missing values. Synthetic simulations are first performed in order to demonstrate the ability of missing data imputation of the extended EM-EOF method in cases of complex displacement, gaps and noise behaviors. Then, the method is applied to time series of offset tracking displacement measurement of Sentinel-2 images acquired between January 2017 and September 2019 over Fox Glacier in the Southern Alps of New Zealand. Promising results confirm the efficiency of the extended EM-EOF method in missing data imputation of satellite image derived displacement time series.</p>

Legal Providing of Application of Energy Effective Lightning Technology and Intellectual Networks in the Conditions of Digital Economy

2019 ◽  
pp. 123-128 ◽  
Author(s):  
Maksim V. Demchenko ◽  
Rostislav O. Ruchkin ◽  
Eugenia P. Simaeva
Keyword(s):  
Public Relations ◽  
New Technologies ◽  
Electric Power Industry ◽  
Legal Regulation ◽  
Power Grids ◽  
Smart Power ◽  
Smart Power Grids ◽  
Russian Economy ◽  
Intellectual Networks ◽  
Efficient Power

The article substantiates the expediency of improving the legal support for the introduction and use of energy-efficient lighting equipment, as well as smart networks (Smart Grid), taking into account the ongoing digitalization of the Russian economy and electric power industry. The goal of scientific research is formulated, which is to develop practical recommendations on optimization of the public relations legal regulation in the digital power engineering sector. The research methodology is represented by the interaction of the legal and sociological aspects of the scientific methods system. The current regulatory and legal basis for the transformation of digital electricity relations has been determined. The need to modernize the system of the new technologies introduction legal regulation for generation, storage, transmission of energy, intelligent networks, including a riskbased management model, is established. A set of standardsetting measures was proposed to transform the legal regulation of public relations in the field of energyefficient lighting equipment with the aim of creating and effectively operating a single digital environment, both at the Federal and regional levels. A priority is set for the development of “smart” power grids and highly efficient power equipment in the constituent entities of the Russian Federation through a set of legal, economic (financial), edu cational measures.

Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring

2021 ◽  
pp. 147592172110219
Author(s):  
Huachen Jiang ◽  
Chunfeng Wan ◽  
Kang Yang ◽  
Youliang Ding ◽  
Songtao Xue
Keyword(s):  
Missing Data ◽  
Health Monitoring ◽  
Signal Transmission ◽  
Imputation Accuracy ◽  
Generative Adversarial Networks ◽  
Data Imputation ◽  
Sensor Failure ◽  
Generative Adversarial Network ◽  
Missing Data Imputation ◽  
Adversarial Network

Wireless sensors are the key components of structural health monitoring systems. During the signal transmission, sensor failure is inevitable, among which, data loss is the most common type. Missing data problem poses a huge challenge to the consequent damage detection and condition assessment, and therefore, great importance should be attached. Conventional missing data imputation basically adopts the correlation-based method, especially for strain monitoring data. However, such methods often require delicate model selection, and the correlations for vehicle-induced strains are much harder to be captured compared with temperature-induced strains. In this article, a novel data-driven generative adversarial network (GAN) for imputing missing strain response is proposed. As opposed to traditional ways where correlations for inter-strains are explicitly modeled, the proposed method directly imputes the missing data considering the spatial–temporal relationships with other strain sensors based on the remaining observed data. Furthermore, the intact and complete dataset is not even necessary during the training process, which shows another great superiority over the model-based imputation method. The proposed method is implemented and verified on a real concrete bridge. In order to demonstrate the applicability and robustness of the GAN, imputation for single and multiple sensors is studied. Results show the proposed method provides an excellent performance of imputation accuracy and efficiency.

scholarly journals Kernel weighted least square approach for imputing missing values of metabolomics data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nishith Kumar ◽  
Md. Aminul Hoque ◽  
Masahiro Sugimoto
Keyword(s):  
Missing Data ◽  
Large Scale ◽  
Missing Values ◽  
Kernel Weight ◽  
Least Square ◽  
Data Matrix ◽  
Data Imputation ◽  
Metabolomics Data ◽  
Missing Value ◽  
Missing Data Imputation

AbstractMass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA.

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